PMLE Practice Test 23 — 15 Questions

Question 1

An organization is deploying a loan approval model and wants to monitor for fairness across demographic subgroups. They have ground truth labels stored in BigQuery. How can they use Vertex AI to evaluate performance disparities between groups?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Upload predictions and labels to Vertex AI Model Evaluation and specify slicing columns. Vertex AI Model Evaluation provides sliced evaluation, which computes metrics (e.g., accuracy, precision) per subgroup when slicing columns are specified. This enables detection of performance disparities.

Answer

Query BigQuery and manually compute metrics per group, then visualize in Looker

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Enable Vertex AI Model Monitoring with fairness detection

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Use Vertex AI Explainable AI to get feature attributions per subgroup

Question 2

A machine learning engineer needs to run batch predictions on 50 TB of data stored in BigQuery using a Vertex AI model. The model is a custom container. What is the most efficient way to set up the batch prediction job?

Accepted Answer

Create a Vertex AI batch prediction job with BigQuery source and BigQuery destination.. Vertex AI batch prediction supports BigQuery as both input and output source, which is the most direct approach. Dataflow preprocessing is optional only if needed.

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Use Dataflow to process the data and call the model via Vertex AI online prediction.

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Export BigQuery data to CSV in GCS, then create a batch prediction job with GCS source.

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Create a Cloud Function to iterate over BigQuery rows and call the endpoint.

Question 3

A company has a model serving predictions on Vertex AI Endpoints and wants to monitor for prediction drift. They enable Vertex AI Model Monitoring but also need to see a confusion matrix over time. How should they set up the confusion matrix monitoring?

Accepted Answer

Upload ground truth data to BigQuery and use Vertex AI Model Monitoring's model quality monitoring. Vertex AI Model Monitoring can compute confusion matrices when ground truth labels are provided in BigQuery. The service compares predictions against actuals and can output confusion matrices over time windows.

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Use Cloud Monitoring to create a custom dashboard with a confusion matrix chart

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Export predictions to Cloud Storage and run a Dataflow job to compute confusion matrices

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Enable Vertex AI Explainable AI and configure it to output confusion matrices

Question 4

What is the purpose of the 'importer' component in Vertex AI Pipelines?

Accepted Answer

To import existing ML artifacts (e.g., models, datasets) into a pipeline as inputs.. The importer component allows you to bring existing artifacts (e.g., a model in Vertex AI Model Registry) into a pipeline for downstream use.

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To import data from external sources into the pipeline.

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To import Python libraries into the pipeline environment.

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To import pipeline definitions from other projects.

Question 5

You need to deploy a model for online predictions with low latency. You want to ensure that the endpoint can handle traffic bursts without cold start. Which TWO configurations should you set? (Choose 2)

Accepted Answer

Set minReplicas to 1.. To avoid cold start, you must keep at least one replica always running (minReplicas ≥ 1) and ensure that the machine type has enough capacity. Setting minReplicas to 0 would cause cold start. Also, setting a higher CPU utilisation target may help but not directly avoid cold start.

Answer

Set maxReplicas to a high number to handle bursts.

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Deploy the model as a custom container.

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Enable autoscaling with a target CPU utilisation of 30%.

Question 6

A company is using Vertex AI Prediction with a custom container that performs preprocessing before inference. The preprocessing step is CPU-intensive and the inference step uses a GPU. They want to minimize prediction latency while optimizing cost. Which architecture should they use?

Accepted Answer

Use a single GPU machine (e.g., n1-standard-4 with T4) and perform both preprocessing and inference on the same instance.. Using a CPU-only node for preprocessing and then sending the preprocessed data to a GPU node for inference separates concerns and allows independent scaling, but adds network latency. The best approach is to use a single machine with both CPU and GPU to avoid network round-trip, and to adjust the machine type to have enough CPU resources.

Answer

Use Cloud Run for preprocessing and send HTTP requests to a GPU-backed Vertex AI endpoint for inference.

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Use two separate Vertex AI endpoints: one CPU-based for preprocessing, one GPU-based for inference, and chain them with Cloud Tasks.

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Use Dataflow for preprocessing and then invoke the model, but Dataflow is not designed for real-time prediction.

Question 7

A team is monitoring a deployed model and notices that the prediction distribution has changed significantly over the last week. They want to detect which features are contributing most to the drift. Which tool should they use?

Accepted Answer

Vertex AI Explainable AI. Vertex AI Explainable AI provides feature attributions (e.g., SHAP values) that can be used to identify which features are most important for predictions. By comparing feature importance over time, they can pinpoint which features are drifting.

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Vertex AI Feature Store

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Vertex AI Model Monitoring

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Vertex AI Pipelines

Question 8

An organization wants to deploy a model on edge devices (e.g., Android phones) for offline inference. They trained a model using TensorFlow. Which THREE steps should they take to prepare and deploy the model?

Accepted Answer

Convert the model to TensorFlow Lite format.. For edge deployment on Android, you need to convert the model to TensorFlow Lite, use Vertex AI Edge Manager to manage the deployment, and then deploy the TFLite model to the devices. Exporting to ONNX is for other platforms, and creating a REST endpoint is for online serving.

Answer

Deploy the model to a Vertex AI endpoint for online inference.

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Export the model to ONNX format.

Question 9

You need to deploy a PyTorch model for online inference on Vertex AI but the model was trained using custom ops that are not natively supported. You want to use NVIDIA Triton Inference Server for optimisation. How should you proceed?

Accepted Answer

Build a custom container with NVIDIA Triton Inference Server and deploy it to Vertex AI.. Vertex AI supports deploying models with NVIDIA Triton Inference Server. You can build a custom container with Triton and the model, then deploy it to a Vertex AI endpoint. This allows using Triton's optimisations.

Answer

Convert the model to TFLite and deploy on an edge device.

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Export the model to ONNX and deploy using Vertex AI's built-in TensorFlow serving.

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Use Vertex AI Model Optimisation to automatically quantise the model.

Question 10

An ML engineer needs to set up automated retraining triggered by data drift. They have decided to use Cloud Monitoring alerts to detect drift. Which TWO additional services are required to complete the retraining pipeline? (Choose 2)

Accepted Answer

Vertex AI Pipeline. The typical architecture uses Cloud Monitoring alert -> Pub/Sub -> Cloud Functions -> Vertex AI Pipeline. Cloud Functions processes the alert and triggers the pipeline. Vertex AI Pipeline orchestrates training and deployment.

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Cloud Dataflow

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Cloud Build

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Cloud Scheduler

Question 11

A data scientist has deployed a classification model on a Vertex AI Endpoint and wants to monitor for feature drift in the serving data compared to the training data. Which Vertex AI service should be used?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is specifically designed to monitor deployed models for feature skew, feature drift, and prediction drift. It supports algorithms like Jensen-Shannon divergence and Population Stability Index.

Answer

Vertex AI Explainable AI

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Vertex AI Model Evaluation

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Vertex AI Continuous Training

Question 12

A team wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in a Cloud Storage bucket. Which combination of services should they use?

Accepted Answer

Cloud Storage → Cloud Functions → Vertex AI Pipelines. Event-driven triggers for Vertex AI pipelines are typically implemented using Cloud Storage notifications to Pub/Sub, which triggers a Cloud Function that creates a pipeline job. Cloud Build is for CI/CD, not for event-driven data triggers.

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Cloud Storage → BigQuery → Vertex AI Pipelines

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Cloud Storage → Cloud Build → Vertex AI Pipelines

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Cloud Storage → Cloud Scheduler → Vertex AI Pipelines

Question 13

A team is deploying a large PyTorch model for online inference. They want to use NVIDIA Triton Inference Server to optimize serving performance. How can they integrate Triton with Vertex AI?

Accepted Answer

Package the model with Triton in a custom container and deploy it to Vertex AI. Vertex AI supports custom containers; you can build a Docker image with Triton Inference Server and deploy it as a model on Vertex AI.

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Vertex AI automatically uses Triton for all PyTorch models

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Deploy the model to GKE and use Vertex AI as a frontend

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Use a prebuilt Vertex AI PyTorch container that includes Triton

Question 14

A team has deployed a model on Vertex AI and wants to cache frequent identical prediction requests to improve latency and reduce cost. Which Google Cloud service should they use?

Accepted Answer

Cloud Memorystore. Cloud Memorystore (Redis) can be used as a cache. The application hashes the request and checks the cache before calling the model.

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Cloud Bigtable

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Cloud CDN

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Cloud SQL

Question 15

A company has a CI/CD pipeline that retrains a model every time new training data is available. They want to automatically deploy the new model to production only if it passes a set of evaluation tests on a staging environment. Which approach best implements this?

Accepted Answer

Implement a two-stage pipeline: train and deploy to staging, run evaluation tests, and if passed, deploy to production using conditional logic.. A continuous delivery pipeline with evaluation gates uses separate stages: staging deployment → evaluation → promotion to production.

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Use Cloud Build to trigger a training job and then a separate deployment job without evaluation.

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Use a single Vertex AI pipeline that trains and deploys to staging, then manually promote.

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Train and deploy directly to production in one pipeline.